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WABI
2010
Springer
167views Bioinformatics» more  WABI 2010»
13 years 6 months ago
Quantifying the Strength of Natural Selection of a Motif Sequence
Quantification of selective pressures on regulatory sequences is a central question in studying the evolution of gene regulatory networks. Previous methods focus primarily on sing...
Chen-Hsiang Yeang
IMSCCS
2006
IEEE
14 years 1 months ago
Frequency Distribution of TATA Box and Extension Sequences on Human Promoters
Background: TATA box is one of the most important transcription factor binding sites. But the exact sequences of TATA box are still not very clear. Results: In this study, we cond...
Wei Shi, Wanlei Zhou
ICMLA
2007
13 years 9 months ago
SVMotif: A Machine Learning Motif Algorithm
We describe SVMotif, a support vector machine-based learning algorithm for identification of cellular DNA transcription factor (TF) motifs extrapolated from known TF-gene interact...
Mark A. Kon, Yue Fan, Dustin T. Holloway, Charles ...
RECOMB
2002
Springer
14 years 7 months ago
From promoter sequence to expression: a probabilistic framework
We present a probabilistic framework that models the process by which transcriptional binding explains the mRNA expression of different genes. Our joint probabilistic model unifie...
Eran Segal, Yoseph Barash, Itamar Simon, Nir Fried...
BMCBI
2007
169views more  BMCBI 2007»
13 years 7 months ago
Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and i
Background: Gene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targ...
Abdallah Sayyed-Ahmad, Kagan Tuncay, Peter J. Orto...